Early warning of tunnel collapse based on Adam-optimised long short-term memory network and TBM operation parameters

Hou, Shaokang; Liu, Yaoru

doi:10.1016/j.engappai.2022.104842

Cited by 34 publications

(1 citation statement)

References 43 publications

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“…The optimal number of hidden neurons in each hidden layer is determined to be 64, with ReLU serving as the activation function. The Adam optimizer, leveraging the benefits of RMSProp and AdaGrad, updates the weight matrices and bias vectors [43]. Given that a larger batch size can improve gradient estimates but may extend training time, a batch size of 20 is chosen for this study.…”

Section: Model Constructionmentioning

confidence: 99%

BiLSTM for Predicting Post-Construction Subsoil Settlement under Embankment: Advancing Sustainable Infrastructure

Wang,

Li,

Wang

et al. 2023

Sustainability

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The load and settlement histories of stage-constructed embankments provide critical insights into long-term surface behavior under embankment loading. However, these data often remain underutilized in predicting post-construction settlement in the absence of geotechnical subsoil characterization. To address this limitation, the current study integrates bidirectional long short-term memory (BiLSTM) into a three-phase framework: data preparation, model construction, and performance evaluation. In the data preparation phase, the feature vector comprises basal pressure, pressure increments, time intervals, and prior settlement values to facilitate a rolling forecast. To manage unevenly spaced data, an Akima spline standardizes the desired time intervals. The model’s efficacy is validated using observational data from two distinct construction case studies, each featuring diverse soil conditions. BiLSTM proves effective in identifying key attributes from load and settlement data during the staged construction process. Compared to traditional curve-fitting methods, the BiLSTM model exhibits superior performance, robustness, and adaptability to varying soil conditions. Additionally, the model demonstrates low sensitivity to the range of post-construction data, allowing for a data collection period reduction—from six months to three—without compromising prediction accuracy (relative error = 0.92%). These advantages not only optimize resource allocation but also contribute to broader sustainability objectives.

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Section: Model Constructionmentioning

confidence: 99%